DETAILED ACTION
This is in reference to communication received 11 December 2025. Claims 1 – 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1, representative of claims 15 and 18, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to a
receiving approval data indicative of a pool of individuals for whom inclusion in a campaign of a content sponsor has been approved by the content sponsor; when determination is made that content of the content sponsor is to be presented to a user of a client device, one of the individual is selected to be included in a content item of the content sponsor and added to the content to a salient area of the content thereby modifying the content and provided to the client device.
These limitations describe marketing/sales/advertising activities. When it is determined that content of the content sponsor is to be presented to a client-user, before presenting the content of the content-sponsor, a content of the sponsor that need to be presented to the client-user is modified by adding supplemental-content like an approved image authorized by the content-sponsor by affixing it in a salient area of the mage in the content and presented to the client user as claimed would be part of sales and marketing activities. Causing presentation of the modified content of a content-sponsor to a client would be the marketing team (or person) providing, such as a visual presentation, the valuation information to the identified client-user (the first or second merchant).
In addition, Generative AI model is used to identify the salient area on the image and add the supplemental-content in the salient area as claimed would also be part of the sales and marketing team (or person) automating their content modifying functions to produce modified visual presentation for presentation to the client-user.
Represented claims 15 and 18, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 15), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 18).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 16, 16 – 17 and 19 – 20 dependent on the aforementioned independent claims, and include all the limitations contained therein. These claims do not recite any additional technical elements, and simply disclose additional limitations that further limit the abstract idea with details regarding Providing an user-interface resembling a dashboard to enable a content-sponsor to manage their campaign, defining that deep-neural-network machine-learning technology, saliency-classification-model, etc. will be used, defining what user activities will be considered to content modifying and content presentation eligibility, and defining that the content will be provided to the user only after they have been selected as an eligible content to receive the content.. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Google-Support published article “How Smart campaigns work” hereinafter referred to as Google-Ads in view of Amazon.com published article “What is a GAN” hereinafter referred to as Amazon-AWS, Jacob Gildenblat published article “Simple Image saliency detection from histogram backprojection” hereinafter referred to as Gildenblat, Wilson US Patent 11,961,124 and Shiva Mayahi et al. published article “The Impact of Generative AI on the Future of Visual Content Marketing” hereinafter referred to as Mayahi.
Regarding claim 1 and representative claims 15 and 18, Google-Ads teaches system and method for increasing content interactions of users (Google-Ads, Smart campaigns help you highlight selling points of your business and attract customers. You can create a single campaign for your business, or run multiple campaigns to showcase different products or services your business offers, each with their own set of keyword themes) [Google-Ads, page 2]
the method comprising:
one or more processors (Google-Ads, Google.com); and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors (Google-Ads, Smart campaigns help you highlight selling points of your business and attract customers.) [Google-Ads, page 2], cause the one or more processors for:
receiving, by one or more processors, approval data indicative of a pool of individuals for whom inclusion in a campaign of a content sponsor has been approved by the content sponsor (Google-Ads, When you sign up for a Smart campaign, you’ll write an ad that describes your business. You’ll also choose which keyword themes you want to target your ad and set a budget. Your ad will automatically show to potential customers across Google Search, Google Maps, YouTube, Gmail, and Google partner websites. Your ad can show when potential customers in your geographic area search for phrases related to your business on Google or Google Maps. Your ad can also show for customers who are outside of your neighborhood, but who include terms related to your business as well as your business location in their searches.) [Google-Ads, page 2];
determining, by the one or more processors, that content of the content sponsor is to be presented to a user of a client device (Google-Ads, Your ad can show when potential customers in your geographic area search for phrases related to your business on Google or Google Maps. Your ad can also show for customers who are outside of your neighborhood, but who include terms related to your business as well as your business location in their searches.) [Google-Ads, page 2];
Google does not explicitly teach causing modified content to be served to client device for presentation to the user. However, Amazon-AWS teaches Generative Adversarial Network (GAN) is a deep learning architecture [Amazon-AWS, page 1]. Amazon-AWS teaches Generative adversarial networks create realistic images through text-based prompts or by modifying existing images. They can help create realistic and immersive visual experiences in video games and digital entertainment. GAN can also edit images—like converting a low-resolution image to a high resolution or turning a black-and-white image to color. It can also create realistic faces, characters, and animals for animation and video [Amazon-AWS, page 1].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Google-Ads by adopting teachings of Amazon-AWS and use GAN to create realistic faces, characters, and animals for animation and video.
Google-Ads in view of Amazon-AWS teaches system and method further comprising:
selecting, by the one or more processors and based on one or more user signals representing one or more online activities of the user (Google-Ads, Smart campaigns will show your ad to the right people when they search for or are interested in what you offer across Google Search, Maps, Gmail, YouTube, and even partner websites. And you only pay when people click on your ad or call you from it.) [Google-Ads, page 17], an individual from the pool of individuals to be included in a content item of the content sponsor (Amazon-AWS, e.g., horse and zebra) [Amazon-AWS,page 2];
Google-Ads in view of Amazon-AWS does not explicitly teach identifying of low-saliency area of the content. However, Gildenblat teaches Saliency detection is used in a lot of applications, the most popular of them is probably automatic thumbnail generation, where a descriptive thumbnail has to be generated for an image. Usually a saliency map is generated, where a pixel is bright if it’s salient, and dark otherwise. There are lots of interesting papers on this. The basic idea is that usually salient pixels should have very different colors than most of the other pixels in the image [Gildenblat, page 1].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Google-Ads in view of Amazon-AWS by adopting teachings of Gildenblat and use Saliency for identifying regions using tools like Grabcut as taught by Gildenblat to preprocess the image with Mean Shift for an initial soft segmentation of the image.
Google-Ads in view of Amazon-AWS and Gildenblat teaches system and method further comprising:
generating, by the one or more processors, a modified content item, at least by
identifying bounds of a replaceable region of the content item
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[Gildenblat, page 3], and
Google-Ads in view of Amazon-AWS and Gildenblat does not explicitly teach inserting an image within the identified bounds. However, Wilson teaches system and method for providing non-intrusive advertising content. Wilson teaches receiving user input reflecting user content preferences for receiving desired content related to specific topics in lieu of standard advertising content, receiving a web request for electronic content, a layout associated with a web page displaying the electronic content including specific regions for displaying advertising content, and retrieving the desired content to include in one of the specific regions based on user content preferences [Wilson, col. 2. Lines 1 – 9].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Google-Ads in view of Amazon-AWS and Gildenblat by adopting teachings of Wilson to add non-intrusive advertising content to display along with the desired content.
Google-Ads in view of Amazon-AWS, Gildenblat and Wilson teaches system and method further comprising:
identifying bounds of a replaceable region of the content item [Gildenblat, page 3], and inserting an image of the selected individual within the identified bounds of the content item (Wilson, datagrams may be generated by embedding, watermarking, and/or overlaying the non-intrusive advertising content in/on desired content. Generated datagrams may be served to a user throughout an advertising or publishing network in lieu of display ads.) [Wilson, col. 11, lines 20 – 24];
Google-Ads in view of Amazon-AWS, Gildenblat and Wilson does not teach using generative artificial intelligence model for generating content. However, Mayahi teaches. Artificial intelligence has advanced rapidly in recent years, with innumerable new examples of its uses emerging. The use of artificial intelligence in picture development is a useful stepping stone toward the introduction of artificial intelligence into the world of design and art … The platform, which is based on the most recent artificial intelligence technology, provides a comprehensive process solution of personalized design, customization, and sales support for customers [Mayahi, page 5, 6].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Google-Ads in view of Amazon-AWS, Gildenblat and Wilson by adopting teachings of Mayahi to enhance the ability of marketers to target the right audience and provide personalized customer experiences.
Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method further comprising:
generating, using a generative artificial intelligence (Al) model, surrounding content that fills at least an area between the identified bounds and the inserted image (Mayahi, Generative AI may be used to generate an image for a campaign targeting females (e.g., targeting criteria)) [Mayahi, page 4], and
inserting the surrounding content into the area of the content item between the identified bounds and the inserted image; and
causing, by the one or more processors, the modified content item to be served to the client device for presentation to the user (Google-Ads, Smart campaigns will show your ad to the right people when they search for or are interested in what you offer across Google Search, Maps, Gmail, YouTube, and even partner websites. And you only pay when people click on your ad or call you from it.) [Google-Ads, page 17], an individual from the pool of individuals to be included in a content item of the content sponsor (Amazon-AWS, e.g., horse and zebra) [Amazon-AWS,page 2].
Regarding claim 2, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method further comprising:
providing, by the one or more processors and to computing devices of a plurality of individuals that includes the pool of individuals, an online dashboard presenting
one or more campaigns of one or more content sponsors, and
one or more interactive controls that enable applications for one or more of the one or more campaigns;
(Google-Ads teaches providing Google-Analytics to allow advertisers to see their campaign performance)
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[Google-Ads. [age 15]
receiving, by the one or more processors and from computing devices of at least the pool of individuals, selection data indicating a selection, by each individual of at least the pool of individuals and via the online dashboard, of at least the campaign of the content sponsor (Google-Ads, When you sign up for a Smart campaign, you’ll write an ad that describes your business. You’ll also choose which keyword themes you want to target your ad and set a budget. Your ad will automatically show to potential customers across Google Search, Google Maps, YouTube, Gmail, and Google partner websites.) [Google-Ads, page 2]; and
sending, by the one or more processors and to a computing device associated with the content sponsor, application data indicating at least (i) the pool of individuals and (ii) the campaign (Amazon-AWS, Your ad will automatically show to potential customers across Google Search, Google Maps, YouTube, Gmail, and Google partner websites. Your ad can show when potential customers in your geographic area search for phrases related to your business on Google or Google Maps. Your ad can also show for customers who are outside of your neighborhood, but who include terms related to your business as well as your business location in their searches.) [Google-Ads, page 2],
wherein receiving the approval data includes receiving the approval data indicative of the pool of individuals from the content sponsor in response to sending the application data (Amazon-AWS, If you want to advertise different aspects of your business, you can create multiple Smart campaigns for your business, and specify different keyword themes and budgets for each. For example, a bakery might want to create a general “bakery” campaign, but create an additional campaign for “wedding cakes”. Each of these can have different keyword themes, budgets, and targeting.) [Amazon-AWS, page 4].
Regarding claim 3, 16 and 19, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein selecting the individual includes inputting the one or more user signals into a trained deep neural network (Amazon-AWS, Generative Adversarial Network (GAN) is a deep learning architecture [Amazon-AWS, page 1]. Amazon-AWS teaches Generative adversarial networks create realistic images through text-based prompts or by modifying existing images. They can help create realistic and immersive visual experiences in video games and digital entertainment. GAN can also edit images—like converting a low-resolution image to a high resolution or turning a black-and-white image to color. It can also create realistic faces, characters, and animals for animation and video [Amazon-AWS, page 1].
Regarding claim 4, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein the one or more user signals representing one or more online activities of the user include: data indicative of a subscription of the user (Amazon-AWS, If you want to advertise different aspects of your business, you can create multiple Smart campaigns for your business, and specify different keyword themes and budgets for each. For example, a bakery might want to create a general “bakery” campaign, but create an additional campaign for “wedding cakes”. Each of these can have different keyword themes, budgets, and targeting.) [Amazon-AWS, page 4].
Regarding claim 5, 17 and 20, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein the one or more user signals representing one or more online activities of the user include one or more of:
data indicative of a subscription of the user;
data indicative of one or more videos previously watched by the user;
data indicative of how much or how often the user watched the one or more videos; or
data indicative of a video currently being watched by the user
(Google-Ads teaches providing Google-Analytics to allow advertisers to see their campaign performance)
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[Google-Ads. [age 15].
Regarding claim 6, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein the one or more user signals representing one or more online activities of the user include: data indicative of an information resource currently being accessed by the user via the client device (Google-Ads, Smart campaigns will show your ad to the right people when they search for or are interested in what you offer across Google Search, Maps, Gmail, YouTube, and even partner websites. And you only pay when people click on your ad or call you from it.) [Google-Ads, page 17], an individual from the pool of individuals to be included in a content item of the content sponsor (Amazon-AWS, e.g., horse and zebra) [Amazon-AWS,page 2].
Regarding claim 7, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein selecting the individual is further based on one or more of:
data indicative of the content item;
data indicative of a landing page associated with the content item; or
data indicative of the content sponsor
(Amazon-AWS, Your ad will automatically show to potential customers across Google Search, Google Maps, YouTube, Gmail, and Google partner websites. Your ad can show when potential customers in your geographic area search for phrases related to your business on Google or Google Maps. Your ad can also show for customers who are outside of your neighborhood, but who include terms related to your business as well as your business location in their searches.) [Google-Ads, page 2].
Regarding claim 8, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein identifying the bounds of the replaceable region includes: using a saliency classification model to identify a low-saliency area of the content item in which to insert the image of the selected individual (as responded to above) [Gildenblat, page 3].
Regarding claim 9, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein identifying the bounds of the replaceable region includes: using a digital indication provided by the content sponsor to identify an area of the content item (as responded to above) [Gildenblat, page 3] in which to insert the image of the selected individual (Google-Ads. To help your ad attract more customers and receive more clicks, the information you've provided about your business and the content in your website is used to create and test alternate ads. In some cases, this information may be used to test different combinations of headlines, descriptions, and landing pages. It may also be used to add sitelinks or replace your headlines with your business name, phone number, or address.) [Google-Ads, page 4].
Regarding claim 10, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi system and method, wherein generating the modified content item further includes: generating, using a generative artificial intelligence (AI) model, surrounding content that fills at least an area between the identified bounds (as responded to above) [Gildenblat, page 3] and the inserted image (Google-Ads. To help your ad attract more customers and receive more clicks, the information you've provided about your business and the content in your website is used to create and test alternate ads. In some cases, this information may be used to test different combinations of headlines, descriptions, and landing pages. It may also be used to add sitelinks or replace your headlines with your business name, phone number, or address.) [Google-Ads, page 4].
Regarding claim 11, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein the generative AI model includes an image-generating large language model (LLM) (Amazon-AWS, Generative Adversarial Network (GAN) is a deep learning architecture [Amazon-AWS, page 1]. Amazon-AWS teaches Generative adversarial networks create realistic images through text-based prompts or by modifying existing images. They can help create realistic and immersive visual experiences in video games and digital entertainment. GAN can also edit images—like converting a low-resolution image to a high resolution or turning a black-and-white image to color. It can also create realistic faces, characters, and animals for animation and video [Amazon-AWS, page 1].
Regarding claim 12, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein generating the modified content item further includes: modifying, using a generative artificial intelligence (AI) model [Amazon-AWS], text of the content item based on information associated with the selected individual (Google-Ads. To help your ad attract more customers and receive more clicks, the information you've provided about your business and the content in your website is used to create and test alternate ads. In some cases, this information may be used to test different combinations of headlines, descriptions, and landing pages. It may also be used to add sitelinks or replace your headlines with your business name, phone number, or address.) [Google-Ads, page 4], the text of the content item being outside of the identified bounds (as responded to above) [Gildenblat, page 3].
Regarding claim 13, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein generating the modified content item further includes modifying, using a generative artificial intelligence (AI) model, the image of the individual based on one or both of the content item and the content sponsor (Mayahi, The use of artificial intelligence in picture development is a useful stepping stone toward the introduction of artificial intelligence into the world of design and art … The platform, which is based on the most recent artificial intelligence technology, provides a comprehensive process solution of personalized design, customization, and sales support for customers) [Mayahi, page 5, 6].
Regarding claim 14, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein determining that content of the content sponsor is to be presented to the user of the client device occurs after selecting the individual from the pool of individuals (Google-Ads, Smart campaigns will show your ad to the right people when they search for or are interested in what you offer across Google Search, Maps, Gmail, YouTube, and even partner websites. And you only pay when people click on your ad or call you from it.) [Google-Ads, page 17], an individual from the pool of individuals to be included in a content item of the content sponsor (Amazon-AWS, e.g., horse and zebra) [Amazon-AWS,page 2].
Regarding claim 17 and 20, as combined and under the same rationale as above, Google-Ads in view of Amazon-AWS, Gildenblat, Wilson and Mayahi teaches system and method, wherein the one or more user signals representing one or more online activities of the user include one or more of:
data indicative of a subscription of the user;
data indicative of one or more videos previously watched by the user;
data indicative of how much or how often the user watched the one or more videos; or
data indicative of a video currently being watched by the user
(Google-Ads teaches providing Google-Analytics to allow advertisers to see their campaign performance)
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[Google-Ads. [age 15].
Response to Arguments
Applicant's arguments that pending amended claims are eligible for patent have been fully considered. However, applicant’s arguments are for amended claimed invention which are moot under new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
March 17, 2026